Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Planetary-scale network testbeds like PlanetLab and RON have become indispensable for evaluating prototypes of distributed systems under realistic Internet conditions. However, current testbeds lack the heterogeneity that characterizes the commercial Internet. For example, most testbed nodes are connected to well-provisioned research networks, whereas most Internet nodes are in edge networks. In this paper, we present the design, implementation, and evaluation of SatelliteLab, a testbed that includes nodes from a diverse set of Internet edge networks. SatelliteLab has a two-tier architecture, in which well-provisioned nodes called planets form the core, and lightweight nodes called satellites connect to the planets from the periphery. The application code of an experiment runs on the planets, whereas the satellites only forward network traffic. Thus, the traffic is subjected to the network conditions of the satellites, which greatly improves the testbed's network heterogeneity. The separation of code execution and traffic forwarding enables satellites to remain lightweight, which lowers the barrier to entry for Internet edge nodes. Our prototype of SatelliteLab uses PlanetLab nodes as planets and a set of 32 volunteered satellites with diverse network characteristics. These satellites consist of desktops, laptops, and handhelds connected to the Internet via cable, DSL, ISDN, Wi-Fi, Bluetooth, and cellular links. We evaluate SatelliteLab's design, and we demonstrate the benefits of evaluating applications on SatelliteLab.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it